{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_diabetes\n",
    "import pandas as pd\n",
    "x,y = load_diabetes(return_X_y=True,as_frame=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>bp</th>\n",
       "      <th>s1</th>\n",
       "      <th>s2</th>\n",
       "      <th>s3</th>\n",
       "      <th>s4</th>\n",
       "      <th>s5</th>\n",
       "      <th>s6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.038076</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.061696</td>\n",
       "      <td>0.021872</td>\n",
       "      <td>-0.044223</td>\n",
       "      <td>-0.034821</td>\n",
       "      <td>-0.043401</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.019908</td>\n",
       "      <td>-0.017646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.001882</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.051474</td>\n",
       "      <td>-0.026328</td>\n",
       "      <td>-0.008449</td>\n",
       "      <td>-0.019163</td>\n",
       "      <td>0.074412</td>\n",
       "      <td>-0.039493</td>\n",
       "      <td>-0.068330</td>\n",
       "      <td>-0.092204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.085299</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.044451</td>\n",
       "      <td>-0.005671</td>\n",
       "      <td>-0.045599</td>\n",
       "      <td>-0.034194</td>\n",
       "      <td>-0.032356</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.002864</td>\n",
       "      <td>-0.025930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.089063</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.011595</td>\n",
       "      <td>-0.036656</td>\n",
       "      <td>0.012191</td>\n",
       "      <td>0.024991</td>\n",
       "      <td>-0.036038</td>\n",
       "      <td>0.034309</td>\n",
       "      <td>0.022692</td>\n",
       "      <td>-0.009362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.005383</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.036385</td>\n",
       "      <td>0.021872</td>\n",
       "      <td>0.003935</td>\n",
       "      <td>0.015596</td>\n",
       "      <td>0.008142</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>-0.031991</td>\n",
       "      <td>-0.046641</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        age       sex       bmi        bp        s1        s2        s3  \\\n",
       "0  0.038076  0.050680  0.061696  0.021872 -0.044223 -0.034821 -0.043401   \n",
       "1 -0.001882 -0.044642 -0.051474 -0.026328 -0.008449 -0.019163  0.074412   \n",
       "2  0.085299  0.050680  0.044451 -0.005671 -0.045599 -0.034194 -0.032356   \n",
       "3 -0.089063 -0.044642 -0.011595 -0.036656  0.012191  0.024991 -0.036038   \n",
       "4  0.005383 -0.044642 -0.036385  0.021872  0.003935  0.015596  0.008142   \n",
       "\n",
       "         s4        s5        s6  \n",
       "0 -0.002592  0.019908 -0.017646  \n",
       "1 -0.039493 -0.068330 -0.092204  \n",
       "2 -0.002592  0.002864 -0.025930  \n",
       "3  0.034309  0.022692 -0.009362  \n",
       "4 -0.002592 -0.031991 -0.046641  "
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>bp</th>\n",
       "      <th>s3</th>\n",
       "      <th>s6</th>\n",
       "      <th>random_02</th>\n",
       "      <th>random_05</th>\n",
       "      <th>random_08</th>\n",
       "      <th>random_11</th>\n",
       "      <th>random_14</th>\n",
       "      <th>random_17</th>\n",
       "      <th>...</th>\n",
       "      <th>random_71</th>\n",
       "      <th>random_74</th>\n",
       "      <th>random_77</th>\n",
       "      <th>random_80</th>\n",
       "      <th>random_83</th>\n",
       "      <th>random_86</th>\n",
       "      <th>random_89</th>\n",
       "      <th>random_92</th>\n",
       "      <th>random_95</th>\n",
       "      <th>random_98</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.038076</td>\n",
       "      <td>0.021872</td>\n",
       "      <td>-0.043401</td>\n",
       "      <td>-0.017646</td>\n",
       "      <td>0.647689</td>\n",
       "      <td>-0.234137</td>\n",
       "      <td>-0.469474</td>\n",
       "      <td>-0.465730</td>\n",
       "      <td>-1.724918</td>\n",
       "      <td>0.314247</td>\n",
       "      <td>...</td>\n",
       "      <td>1.538037</td>\n",
       "      <td>-2.619745</td>\n",
       "      <td>-0.299007</td>\n",
       "      <td>-0.219672</td>\n",
       "      <td>-0.518270</td>\n",
       "      <td>0.915402</td>\n",
       "      <td>0.513267</td>\n",
       "      <td>-0.702053</td>\n",
       "      <td>-1.463515</td>\n",
       "      <td>0.005113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.001882</td>\n",
       "      <td>-0.026328</td>\n",
       "      <td>0.074412</td>\n",
       "      <td>-0.092204</td>\n",
       "      <td>-0.342715</td>\n",
       "      <td>0.404051</td>\n",
       "      <td>0.257550</td>\n",
       "      <td>-0.026514</td>\n",
       "      <td>-0.192361</td>\n",
       "      <td>-1.168678</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.815810</td>\n",
       "      <td>0.276691</td>\n",
       "      <td>1.453534</td>\n",
       "      <td>0.625667</td>\n",
       "      <td>0.482472</td>\n",
       "      <td>0.473238</td>\n",
       "      <td>-1.514847</td>\n",
       "      <td>0.214094</td>\n",
       "      <td>0.385317</td>\n",
       "      <td>0.058209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.085299</td>\n",
       "      <td>-0.005671</td>\n",
       "      <td>-0.032356</td>\n",
       "      <td>-0.025930</td>\n",
       "      <td>1.083051</td>\n",
       "      <td>-0.937825</td>\n",
       "      <td>0.515048</td>\n",
       "      <td>1.135566</td>\n",
       "      <td>-0.315269</td>\n",
       "      <td>-0.236819</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.435862</td>\n",
       "      <td>-0.981509</td>\n",
       "      <td>-0.600217</td>\n",
       "      <td>0.113517</td>\n",
       "      <td>-1.237815</td>\n",
       "      <td>-0.151785</td>\n",
       "      <td>-0.622700</td>\n",
       "      <td>-0.589365</td>\n",
       "      <td>-0.692910</td>\n",
       "      <td>0.812862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.089063</td>\n",
       "      <td>-0.036656</td>\n",
       "      <td>-0.036038</td>\n",
       "      <td>-0.009362</td>\n",
       "      <td>0.747294</td>\n",
       "      <td>0.117327</td>\n",
       "      <td>0.547097</td>\n",
       "      <td>1.098777</td>\n",
       "      <td>1.305479</td>\n",
       "      <td>-0.310267</td>\n",
       "      <td>...</td>\n",
       "      <td>0.497998</td>\n",
       "      <td>2.153182</td>\n",
       "      <td>0.183342</td>\n",
       "      <td>-0.839722</td>\n",
       "      <td>-0.525755</td>\n",
       "      <td>0.341756</td>\n",
       "      <td>-0.576904</td>\n",
       "      <td>-1.320233</td>\n",
       "      <td>-0.469176</td>\n",
       "      <td>-0.114540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.005383</td>\n",
       "      <td>0.021872</td>\n",
       "      <td>0.008142</td>\n",
       "      <td>-0.046641</td>\n",
       "      <td>0.005244</td>\n",
       "      <td>0.622850</td>\n",
       "      <td>0.120296</td>\n",
       "      <td>-1.124642</td>\n",
       "      <td>0.332314</td>\n",
       "      <td>0.115675</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.301921</td>\n",
       "      <td>1.644968</td>\n",
       "      <td>0.311250</td>\n",
       "      <td>-0.127918</td>\n",
       "      <td>0.203464</td>\n",
       "      <td>-0.646573</td>\n",
       "      <td>0.881640</td>\n",
       "      <td>0.077368</td>\n",
       "      <td>0.538910</td>\n",
       "      <td>-0.875618</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        age        bp        s3        s6  random_02  random_05  random_08  \\\n",
       "0  0.038076  0.021872 -0.043401 -0.017646   0.647689  -0.234137  -0.469474   \n",
       "1 -0.001882 -0.026328  0.074412 -0.092204  -0.342715   0.404051   0.257550   \n",
       "2  0.085299 -0.005671 -0.032356 -0.025930   1.083051  -0.937825   0.515048   \n",
       "3 -0.089063 -0.036656 -0.036038 -0.009362   0.747294   0.117327   0.547097   \n",
       "4  0.005383  0.021872  0.008142 -0.046641   0.005244   0.622850   0.120296   \n",
       "\n",
       "   random_11  random_14  random_17  ...  random_71  random_74  random_77  \\\n",
       "0  -0.465730  -1.724918   0.314247  ...   1.538037  -2.619745  -0.299007   \n",
       "1  -0.026514  -0.192361  -1.168678  ...  -0.815810   0.276691   1.453534   \n",
       "2   1.135566  -0.315269  -0.236819  ...  -1.435862  -0.981509  -0.600217   \n",
       "3   1.098777   1.305479  -0.310267  ...   0.497998   2.153182   0.183342   \n",
       "4  -1.124642   0.332314   0.115675  ...  -2.301921   1.644968   0.311250   \n",
       "\n",
       "   random_80  random_83  random_86  random_89  random_92  random_95  random_98  \n",
       "0  -0.219672  -0.518270   0.915402   0.513267  -0.702053  -1.463515   0.005113  \n",
       "1   0.625667   0.482472   0.473238  -1.514847   0.214094   0.385317   0.058209  \n",
       "2   0.113517  -1.237815  -0.151785  -0.622700  -0.589365  -0.692910   0.812862  \n",
       "3  -0.839722  -0.525755   0.341756  -0.576904  -1.320233  -0.469176  -0.114540  \n",
       "4  -0.127918   0.203464  -0.646573   0.881640   0.077368   0.538910  -0.875618  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "rng = np.random.RandomState(42)\n",
    "n_random_features = 100\n",
    "X_random = pd.DataFrame(\n",
    "    rng.randn(x.shape[0], n_random_features),\n",
    "    columns=[f\"random_{i:02d}\" for i in range(n_random_features)],\n",
    ")\n",
    "x = pd.concat([x, X_random], axis=1)\n",
    "# Show only a subset of the columns\n",
    "x[x.columns[::3]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.信息选择标准：AIC，BIC。但是怎么控制备选alpha的数量(似乎是通过最大迭代次数)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LassoLarsIC\n",
    "from sklearn.pipeline import make_pipeline\n",
    "lasso_ic = make_pipeline(\n",
    "    StandardScaler(),LassoLarsIC(\"aic\",normalize=False,max_iter=500)\n",
    ").fit(x,y)\n",
    "results = pd.DataFrame(\n",
    "    {\n",
    "        \"alphas\": lasso_ic[-1].alphas_,\n",
    "        \"AIC criterion\": lasso_ic[-1].criterion_,\n",
    "    }\n",
    ").set_index(\"alphas\")\n",
    "alpha_aic = lasso_ic[-1].alpha_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "113"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(lasso_ic[-1].criterion_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "lasso_ic.set_params(lassolarsic__criterion=\"bic\").fit(x, y)\n",
    "results[\"BIC criterion\"] = lasso_ic[-1].criterion_\n",
    "alpha_bic = lasso_ic[-1].alpha_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到AIC与BIC选择结果不一致,BIC更加关注模型简洁性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table id=\"T_a148c\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_a148c_level0_col0\" class=\"col_heading level0 col0\" >AIC criterion</th>\n",
       "      <th id=\"T_a148c_level0_col1\" class=\"col_heading level0 col1\" >BIC criterion</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >alphas</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_a148c_level0_row0\" class=\"row_heading level0 row0\" >45.160030</th>\n",
       "      <td id=\"T_a148c_row0_col0\" class=\"data row0 col0\" >5249.445139</td>\n",
       "      <td id=\"T_a148c_row0_col1\" class=\"data row0 col1\" >5249.445139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a148c_level0_row1\" class=\"row_heading level0 row1\" >42.300448</th>\n",
       "      <td id=\"T_a148c_row1_col0\" class=\"data row1 col0\" >5212.551529</td>\n",
       "      <td id=\"T_a148c_row1_col1\" class=\"data row1 col1\" >5216.642839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a148c_level0_row2\" class=\"row_heading level0 row2\" >21.542302</th>\n",
       "      <td id=\"T_a148c_row2_col0\" class=\"data row2 col0\" >4929.529748</td>\n",
       "      <td id=\"T_a148c_row2_col1\" class=\"data row2 col1\" >4937.712368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a148c_level0_row3\" class=\"row_heading level0 row3\" >15.034110</th>\n",
       "      <td id=\"T_a148c_row3_col0\" class=\"data row3 col0\" >4870.590108</td>\n",
       "      <td id=\"T_a148c_row3_col1\" class=\"data row3 col1\" >4882.864038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a148c_level0_row4\" class=\"row_heading level0 row4\" >6.451336</th>\n",
       "      <td id=\"T_a148c_row4_col0\" class=\"data row4 col0\" >4816.793698</td>\n",
       "      <td id=\"T_a148c_row4_col1\" class=\"data row4 col1\" >4833.158938</td>\n",
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       "      <th id=\"T_a148c_level0_row5\" class=\"row_heading level0 row5\" >6.282822</th>\n",
       "      <td id=\"T_a148c_row5_col0\" class=\"data row5 col0\" >4817.745083</td>\n",
       "      <td id=\"T_a148c_row5_col1\" class=\"data row5 col1\" >4838.201632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a148c_level0_row6\" class=\"row_heading level0 row6\" >6.177710</th>\n",
       "      <td id=\"T_a148c_row6_col0\" class=\"data row6 col0\" >4818.891626</td>\n",
       "      <td id=\"T_a148c_row6_col1\" class=\"data row6 col1\" >4843.439485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a148c_level0_row7\" class=\"row_heading level0 row7\" >5.709002</th>\n",
       "      <td id=\"T_a148c_row7_col0\" class=\"data row7 col0\" >4815.221999</td>\n",
       "      <td id=\"T_a148c_row7_col1\" class=\"data row7 col1\" >4843.861168</td>\n",
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       "      <td id=\"T_a148c_row65_col1\" class=\"data row65 col1\" >5092.963330</td>\n",
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       "      <th id=\"T_a148c_level0_row69\" class=\"row_heading level0 row69\" >1.281445</th>\n",
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       "      <th id=\"T_a148c_level0_row78\" class=\"row_heading level0 row78\" >0.963064</th>\n",
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       "    <tr>\n",
       "      <th id=\"T_a148c_level0_row82\" class=\"row_heading level0 row82\" >0.831952</th>\n",
       "      <td id=\"T_a148c_row82_col0\" class=\"data row82 col0\" >4840.394717</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_a148c_level0_row83\" class=\"row_heading level0 row83\" >0.811099</th>\n",
       "      <td id=\"T_a148c_row83_col0\" class=\"data row83 col0\" >4841.749798</td>\n",
       "      <td id=\"T_a148c_row83_col1\" class=\"data row83 col1\" >5181.328519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a148c_level0_row84\" class=\"row_heading level0 row84\" >0.788028</th>\n",
       "      <td id=\"T_a148c_row84_col0\" class=\"data row84 col0\" >4843.052934</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_a148c_level0_row85\" class=\"row_heading level0 row85\" >0.779397</th>\n",
       "      <td id=\"T_a148c_row85_col0\" class=\"data row85 col0\" >4844.785478</td>\n",
       "      <td id=\"T_a148c_row85_col1\" class=\"data row85 col1\" >5192.546818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a148c_level0_row86\" class=\"row_heading level0 row86\" >0.774433</th>\n",
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       "      <td id=\"T_a148c_row87_col0\" class=\"data row87 col0\" >4844.598063</td>\n",
       "      <td id=\"T_a148c_row87_col1\" class=\"data row87 col1\" >5200.542023</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_a148c_level0_row88\" class=\"row_heading level0 row88\" >0.619845</th>\n",
       "      <td id=\"T_a148c_row88_col0\" class=\"data row88 col0\" >4846.161392</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_a148c_level0_row89\" class=\"row_heading level0 row89\" >0.571993</th>\n",
       "      <td id=\"T_a148c_row89_col0\" class=\"data row89 col0\" >4846.919557</td>\n",
       "      <td id=\"T_a148c_row89_col1\" class=\"data row89 col1\" >5211.046136</td>\n",
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       "      <th id=\"T_a148c_level0_row90\" class=\"row_heading level0 row90\" >0.569664</th>\n",
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       "    <tr>\n",
       "      <th id=\"T_a148c_level0_row91\" class=\"row_heading level0 row91\" >0.559045</th>\n",
       "      <td id=\"T_a148c_row91_col0\" class=\"data row91 col0\" >4850.586038</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a148c_level0_row92\" class=\"row_heading level0 row92\" >0.490549</th>\n",
       "      <td id=\"T_a148c_row92_col0\" class=\"data row92 col0\" >4850.918520</td>\n",
       "      <td id=\"T_a148c_row92_col1\" class=\"data row92 col1\" >5227.319029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a148c_level0_row93\" class=\"row_heading level0 row93\" >0.490339</th>\n",
       "      <td id=\"T_a148c_row93_col0\" class=\"data row93 col0\" >4852.913732</td>\n",
       "      <td id=\"T_a148c_row93_col1\" class=\"data row93 col1\" >5233.405551</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_a148c_level0_row94\" class=\"row_heading level0 row94\" >0.484648</th>\n",
       "      <td id=\"T_a148c_row94_col0\" class=\"data row94 col0\" >4854.783408</td>\n",
       "      <td id=\"T_a148c_row94_col1\" class=\"data row94 col1\" >5239.366537</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_a148c_level0_row95\" class=\"row_heading level0 row95\" >0.449181</th>\n",
       "      <td id=\"T_a148c_row95_col0\" class=\"data row95 col0\" >4855.996886</td>\n",
       "      <td id=\"T_a148c_row95_col1\" class=\"data row95 col1\" >5244.671324</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_a148c_level0_row96\" class=\"row_heading level0 row96\" >0.430694</th>\n",
       "      <td id=\"T_a148c_row96_col0\" class=\"data row96 col0\" >4857.607012</td>\n",
       "      <td id=\"T_a148c_row96_col1\" class=\"data row96 col1\" >5250.372760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a148c_level0_row97\" class=\"row_heading level0 row97\" >0.381536</th>\n",
       "      <td id=\"T_a148c_row97_col0\" class=\"data row97 col0\" >4858.614112</td>\n",
       "      <td id=\"T_a148c_row97_col1\" class=\"data row97 col1\" >5255.471171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a148c_level0_row98\" class=\"row_heading level0 row98\" >0.360391</th>\n",
       "      <td id=\"T_a148c_row98_col0\" class=\"data row98 col0\" >4860.221661</td>\n",
       "      <td id=\"T_a148c_row98_col1\" class=\"data row98 col1\" >5261.170029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a148c_level0_row99\" class=\"row_heading level0 row99\" >0.349131</th>\n",
       "      <td id=\"T_a148c_row99_col0\" class=\"data row99 col0\" >4862.017936</td>\n",
       "      <td id=\"T_a148c_row99_col1\" class=\"data row99 col1\" >5267.057615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a148c_level0_row100\" class=\"row_heading level0 row100\" >0.322583</th>\n",
       "      <td id=\"T_a148c_row100_col0\" class=\"data row100 col0\" >4863.554047</td>\n",
       "      <td id=\"T_a148c_row100_col1\" class=\"data row100 col1\" >5272.685035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a148c_level0_row101\" class=\"row_heading level0 row101\" >0.283392</th>\n",
       "      <td id=\"T_a148c_row101_col0\" class=\"data row101 col0\" >4864.925709</td>\n",
       "      <td id=\"T_a148c_row101_col1\" class=\"data row101 col1\" >5278.148007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a148c_level0_row102\" class=\"row_heading level0 row102\" >0.257014</th>\n",
       "      <td id=\"T_a148c_row102_col0\" class=\"data row102 col0\" >4866.547385</td>\n",
       "      <td id=\"T_a148c_row102_col1\" class=\"data row102 col1\" >5283.860993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a148c_level0_row103\" class=\"row_heading level0 row103\" >0.184479</th>\n",
       "      <td id=\"T_a148c_row103_col0\" class=\"data row103 col0\" >4866.519374</td>\n",
       "      <td id=\"T_a148c_row103_col1\" class=\"data row103 col1\" >5287.924291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a148c_level0_row104\" class=\"row_heading level0 row104\" >0.158897</th>\n",
       "      <td id=\"T_a148c_row104_col0\" class=\"data row104 col0\" >4865.961767</td>\n",
       "      <td id=\"T_a148c_row104_col1\" class=\"data row104 col1\" >5287.366684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a148c_level0_row105\" class=\"row_heading level0 row105\" >0.142040</th>\n",
       "      <td id=\"T_a148c_row105_col0\" class=\"data row105 col0\" >4865.769229</td>\n",
       "      <td id=\"T_a148c_row105_col1\" class=\"data row105 col1\" >5287.174147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a148c_level0_row106\" class=\"row_heading level0 row106\" >0.125524</th>\n",
       "      <td id=\"T_a148c_row106_col0\" class=\"data row106 col0\" >4867.600737</td>\n",
       "      <td id=\"T_a148c_row106_col1\" class=\"data row106 col1\" >5293.096965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a148c_level0_row107\" class=\"row_heading level0 row107\" >0.108990</th>\n",
       "      <td id=\"T_a148c_row107_col0\" class=\"data row107 col0\" >4869.450436</td>\n",
       "      <td id=\"T_a148c_row107_col1\" class=\"data row107 col1\" >5299.037974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a148c_level0_row108\" class=\"row_heading level0 row108\" >0.101203</th>\n",
       "      <td id=\"T_a148c_row108_col0\" class=\"data row108 col0\" >4871.385832</td>\n",
       "      <td id=\"T_a148c_row108_col1\" class=\"data row108 col1\" >5305.064679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a148c_level0_row109\" class=\"row_heading level0 row109\" >0.094396</th>\n",
       "      <td id=\"T_a148c_row109_col0\" class=\"data row109 col0\" >4873.330690</td>\n",
       "      <td id=\"T_a148c_row109_col1\" class=\"data row109 col1\" >5311.100848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a148c_level0_row110\" class=\"row_heading level0 row110\" >0.076694</th>\n",
       "      <td id=\"T_a148c_row110_col0\" class=\"data row110 col0\" >4874.978632</td>\n",
       "      <td id=\"T_a148c_row110_col1\" class=\"data row110 col1\" >5316.840099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a148c_level0_row111\" class=\"row_heading level0 row111\" >0.035975</th>\n",
       "      <td id=\"T_a148c_row111_col0\" class=\"data row111 col0\" >4876.431724</td>\n",
       "      <td id=\"T_a148c_row111_col1\" class=\"data row111 col1\" >5322.384501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a148c_level0_row112\" class=\"row_heading level0 row112\" >0.000000</th>\n",
       "      <td id=\"T_a148c_row112_col0\" class=\"data row112 col0\" >4878.277002</td>\n",
       "      <td id=\"T_a148c_row112_col1\" class=\"data row112 col1\" >5328.321089</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1ec7af35df0>"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def highlight_min(x):\n",
    "    x_min = x.min()\n",
    "    return [\"font-weight: bold\" if v == x_min else \"\" for v in x]\n",
    "\n",
    "\n",
    "results.style.apply(highlight_min)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1ec7b170f10>"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = results.plot()\n",
    "ax.vlines(\n",
    "    alpha_aic,\n",
    "    results[\"AIC criterion\"].min(),\n",
    "    results[\"AIC criterion\"].max(),\n",
    "    label=\"alpha: AIC estimate\",\n",
    "    linestyles=\"--\",\n",
    "    color=\"tab:blue\",\n",
    ")\n",
    "ax.vlines(\n",
    "    alpha_bic,\n",
    "    results[\"BIC criterion\"].min(),\n",
    "    results[\"BIC criterion\"].max(),\n",
    "    label=\"alpha: BIC estimate\",\n",
    "    linestyle=\"--\",\n",
    "    color=\"tab:orange\",\n",
    ")\n",
    "ax.set_xlabel(r\"$\\alpha$\")\n",
    "ax.set_ylabel(\"criterion\")\n",
    "ax.set_xscale(\"log\") # 对数转换\n",
    "ax.legend()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "信息准则得到的2R方"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.        , 23.95200596,  8.94013699,  0.        ,\n",
       "        0.        , -5.26667729,  0.        , 20.8216194 ,  0.        ,\n",
       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ])"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso_ic.score(x,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.交叉验证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LassoCV\n",
    "import matplotlib.pyplot as plt\n",
    "model = make_pipeline(StandardScaler(),LassoCV(cv=50)).fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5072919800208326"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ymin, ymax = 2300, 3800\n",
    "lasso = model[-1]\n",
    "model.score(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.semilogx(lasso.alphas_,lasso.mse_path_,linestyle=\":\")\n",
    "plt.plot(\n",
    "    lasso.alphas_,\n",
    "    lasso.mse_path_.mean(axis=-1),\n",
    "    color=\"black\",\n",
    "    linewidth=2\n",
    ")\n",
    "plt.axvline(lasso.alpha_, linestyle=\"--\", color=\"black\", label=\"alpha: CV estimate\")\n",
    "plt.ylim(ymin,ymax)\n",
    "plt.ylim(ymin, ymax)\n",
    "plt.xlabel(r\"$\\alpha$\")\n",
    "plt.ylabel(\"Mean square error\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " AIC和交叉验证的结果比较类似\n",
    "在此示例中，两种方法的工作方式相似。样本内超参数选择甚至显示了其在计算性能方面的功效。但是，它只能在样本数量与特征数量相比足够大的情况下使用。\n",
    "\n",
    "这就是为什么通过交叉验证进行超参数优化是一种安全策略的原因：它适用于不同的设置。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "岭回归交叉验证正则化的方法也差不多"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import RidgeCV\n",
    "model = make_pipeline(StandardScaler(),RidgeCV(alphas=np.random.normal(46,0.2,1000),cv=10)).fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "ridge = model[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6319060205970397"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6410632867790762"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "model = LinearRegression().fit(x,y)\n",
    "model.score(x,y)"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "d83df975d3fd7e9cd2b9393f36d258bee244a85bb4d399c6c89ec18ff8dd5f85"
  },
  "kernelspec": {
   "display_name": "Python 3.8.3 64-bit ('data': conda)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
